Dynamic path planning is a critical factor in ensuring the safe flight of unmanned aerial vehicles (UAVs) in complex interference environments. To address the issues of high iteration counts, slow convergence, and dynamic obstacle avoidance in dynamic path planning, this paper proposes a UAV dynamic path planning method based on obstacle motion prediction and improved artificial potential fields (APF). First, for dynamic obstacles, a target detection algorithm based on laser radar and a motion prediction algorithm based on Kalman filtering are designed to estimate dynamic obstacle information. A velocity direction similarity detection method is introduced for local position evasion decisions. Secondly, for static obstacles, a simulated annealing algorithm is introduced to perturb the current state, coupled with a neighborhood optimization function based on target points for dynamic path planning. Simulation results show that the proposed algorithm reduces dynamic obstacle avoidance time by 69% when dealing with static obstacles and reduces obstacle avoidance distance by 19.7% and task duration by 23.6% when dealing with dynamic obstacles, thereby enhancing the safety and efficiency of UAV mission execution.